A review on machine learning techniques for text classification

Kavitha, M.; Prabhavathy, P.

doi:10.1109/iccct53315.2021.9711858

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…To facilitate a comprehensive comparison, five additional models were constructed using widely employed conventional machine learning algorithms, including KNN, logistic regression, naïve Bayes, random forest, and LightGBM [32]. These models were built using the same features and the dataset utilized for our CNN models, ensuring fair comparison and performance…”

Section: Other Machine Learning Modelsmentioning

confidence: 99%

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient’s medical history

Cho,

Yu,

Shin

et al. 2024

BMC Med Inform Decis Mak

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Background Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated using anti-epileptic drugs (AEDs) as the first-line therapy. However, due to the variations in their modes of action, identification of effective AEDs often relies on ad hoc trials, which is particularly challenging for pediatric patients. Thus, there is significant value in computational methods capable of assisting in the selection of AEDs, aiming to minimize unnecessary medication and improve treatment efficacy. Results In this study, we collected 7,507 medical records from 1,000 pediatric epilepsy patients and developed a computational clinical decision-supporting system for AED selection. This system leverages three multi-channel convolutional neural network (CNN) models tailored to three specific AEDs (vigabatrin, prednisolone, and clobazam). Each CNN model predicts whether a respective AED is effective on a given patient or not. The CNN models showed AUROCs of 0.90, 0.80, and 0.92 in 10-fold cross-validation, respectively. Evaluation on a hold-out test dataset further revealed positive predictive values (PPVs) of 0.92, 0.97, and 0.91 for the three respective CNN models, representing that suggested AEDs by our models would be effective in controlling epilepsy with a high accuracy and thereby reducing unnecessary medications for pediatric patients. Conclusion Our CNN models in the system demonstrated high PPVs for the three AEDs, which signifies the potential of our approach to support the clinical decision-making by assisting doctors in recommending effective AEDs within the three AEDs for patients based on their medical history. This would result in a reduction in the number of unnecessary ad hoc attempts to find an effective AED for pediatric epilepsy patients.

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Section: Other Machine Learning Modelsmentioning

confidence: 99%

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient’s medical history

Cho,

Yu,

Shin

et al. 2024

BMC Med Inform Decis Mak

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İngilizce Metinlerin Sınıflandırması İçin Makine Öğrenimi Kullanımı

SHABESTANI,

GEÇİKLİ

2024

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Using classification to retrieve information and extract data from text increases the reader's understanding of the content as well. As a result of advances in technology, new methods have been proposed that not only are highly accurate but also are faster than previous methods. Various factors have been used to classify the text to date, including “Sentiment Analysis, Language Detection, Intent Detection, Spam Detection, and Topic Detection”. In English linguistics, one of the most common problems is classifying texts according to their readability level. In this article, authors have reviewed machine learning use in the classification of English text in terms of difficulty and readability level. Moreover, authors attempt to discuss the drawbacks of the deep learning method in terms of accuracy and speed of action.

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A review on machine learning techniques for text classification

Cited by 2 publications

References 14 publications

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient’s medical history

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient’s medical history

İngilizce Metinlerin Sınıflandırması İçin Makine Öğrenimi Kullanımı

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